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The advancements of genomics and genome projects led to the current paradigm that the blueprint of life is depicted in the genome sequences. To decipher the life system, deductive methods have been applied from genome sequences to genes, transcripts, proteins, organelles, cells, tissues, organs, organisms, and populations. As a result we encountered an astronomical scale of complicated molecular and cellular networks in the life system. There is a way, however, to directly connect the function of a single base pair (bp) in genome sequences to the life system by bypassing all the molecular and cellular labyrinths. "MUTANT" provides the ultimate tool as a bona fide biosimulator for the functional annotation of gene and genome networks. Genetics, with mutations and mutants, is revealing the life system. Mendel deduced the concept of "gene" from a large dataset of the pea phenome. Snell discovered the mouse H2 locus by graft rejection that led to the identification and understanding of the major histocompatibility complex. Many other mouse mutants (i.e., nu, scid, lpr, gld, Sl, and W) provided model systems for the functional characterization of key genes in immunological networks. In this context, "reverse genetics" methods have been developed since the 1980s to systematically produce mutant mice carrying a particular gene of interest, for example, transgenic mice, knockout mice, and gene targeting. Recently, more versatile, large-scale, and high-throughput methods such as ENU mutagenesis and insertional mutagenesis are being used to generate mutant mice. This chapter offers a review of the history and current status of mouse mutagenesis and discusses the value of mouse model systems. [ABSTRACT FROM AUTHOR]

The form of the neutralizing antibody response to human immunodeficiency virus type 1 (HIV-1) and the evolutionary response by the virus are poorly understood. In order for a virus particle (virion) to infect a cell, exterior envelope glycoprotein (gp120) molecules on the virion's surface must interact with receptors on the cell's surface. Antibodies that bind to gp120 may neutralize a virion by interfering with these interactions. Therefore, gp120 is expected to evolve in response to selection by both cell-surface receptors and antibodies. The rate of such adaptation and the constraints imposed by a response to one selective force on the response to the other are unknown. Here, I describe a simulation modeling approach to these problems. The population of viral genomes infecting a single patient is represented by the intensely studied third variable (V3) region of gp120, the main determinant of which chemokine coreceptor a virion uses to enter a cell, and an important target of neutralizing antibodies. Mutation and recombination are applied by realistically simulating the viral replication cycle. Selection by chemokine coreceptors is simulated by taking advantage of the fact that mean site-specific amino acid frequencies are measures of the site-specific marginal fitnesses of amino acids in relation to coreceptor interactions. Selection by antibodies is imposed by simulating the affinity maturation of B-cell lineages that produce neutralizing antibodies to HIV-1 V3. These simulations make clear predictions about the functional cost of adaptation to antibody surveillance, which may help explain the pattern of chemokine coreceptor usage by HIV-1. [ABSTRACT FROM AUTHOR]

We describe some computer models of the immune system and in particular of its response to the HIV infection. Then we introduce our model and show some results of simulations of the AIDS disease progression. [ABSTRACT FROM AUTHOR]

The vertebrate immune system protects the host from harmful encounters with pathogenic microorganisms and other dangerous components of the environment. To do this the immune system must gather information about the ‘nonself' pathogens and by processing this information, initiate an appropriate immunological response. The immune system represents an example of emergent behavior from a complex, multifactorial, adaptive system. To understand emergent behavior we need to know the systems components and the rules that govern the system. To identify the components, immunology research has catalogued and characterized probably all major cell types involved in the innate and adaptive immune response. In the postgenomic world, we are now able to further characterize global changes in cellular gene expression and thereby identify and infer changes in the functional state of the cells. Together this should allow modeling of immune system function. Dendritic cells orchestrate the host immune response. By identifying a pathogen and processing this information through a coordinated differentiation program, phenotypic changes effected in dentritic cells allow appropriate information to be conveyed to the adaptive arm of the immune system, thereby shaping downstream immunological responses. Transcriptional profiling of human and mouse dendritic cell responses to different antigens have demonstrated this functional plasticity. Understanding the regulation of these dendritic cell differentiation states will contribute to computational models of the immune system, and our understanding of the parameters that affect the immune system response to infection. [ABSTRACT FROM AUTHOR]

Protein therapeutics have recently emerged as a viable means of treating chronic diseases and are beginning to rival small-molecule drugs in market share. Although their promise of targeted therapy is a major medical advance, repeated administrations in many cases lead to development of antitherapeutic antibodies that compromise treatment. Multiple sources of immunogenicity are considered in this chapter with a focus on the T-cell-dependent immune response. Development of high-affinity antibodies depends on activation of T helper cells by antigen presentation. Disruption of antigen presentation in an antigen-specific manner would be a rational solution to this problem. Here we present the powerful combination of recombinant protein expression and immunoinformatic and molecular modeling tools as a means of reducing immunogenicity by modification of T-cell epitopes. This approach promises to bring to the clinic safer protein therapeutics both as first- and second-generation products. [ABSTRACT FROM AUTHOR]

Normal adaptive immune responses operate under major histocompatibility complex (MHC) restriction by binding to specific short antigenic peptides. Sequence-structure-function information is critical in facilitating the understanding of principles governing MHC-specific peptide recognition and binding. Three-dimensional structures of bound peptide ligands to MHC receptors are today characterized in great number using X-ray crystallography, offering a rich source of information for structural analysis. By utilizing information derived from available experimental structures, it is possible to predict binders for alleles that have not been studied extensively and offers an alternative to sequence-based approaches that require a large dataset for training. This chapter will introduce the use of structural descriptors, as well as comparative modeling and docking techniques for predicting whether a peptide sequence can bind to a specific MHC allele. [ABSTRACT FROM AUTHOR]

Allergies are a growing health problem in developed and developing countries that result in increased healthcare expenditures. This problem is further compounded by increasing number of allergens found in genetically modified (GM) food and allergens found in unexpected sources (hidden allergens). The importance of allergies has prompted the use of new methods like genomics, proteomics, and microarray in understanding the nature of allergies. These methods have generated large amounts of data that have to be stored, retrieved, and analyzed using bioinformatics approaches. Several specialized public databases have been created in response to increasing allergen data. These specialized databases integrate the various information found in general databases into a coherent set of data and provide bioinformatics tools suitable for further analysis. The resources provided by these databases have paved the way for the creation of specialized bioinformatics tools that allow for the prediction of allergenicity. These prediction tools are crucial in view of the new sources of allergens, namely, hidden allergens and potential allergens in the form of recombinant proteins in GM food. Here we review the bioinformatics resources and tools available for the study of allergenicity. [ABSTRACT FROM AUTHOR]

Quantitative Structure-Activity Relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-Major Histocompatibility Complex (MHC) binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches were built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review two methods - a 2D-QSAR Additive-Partial Least Squares (PLS) and a 3D-QSAR Comparative Molecular Similarity Index Analysis (CoMISA) method - which can identify the sequence dependence of peptide binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The Iterative Self-Consistent (ISC) PLS-based Additive Method is a recently developed extension to the Additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We review a variety of human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-Kk, H2-Kb, and H2-Db HLA-DRB1*0101, HLA-DRB1*0401, and HLA-DRB1*0701, I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek. In terms of reliability the resulting models represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner. ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, are freely available online at: http://www.jenner.ac.uk/ MHCPred. [ABSTRACT FROM AUTHOR]

One of the key elements in the adaptive immune response is the presentation of peptides by the major histocompatibility complex (MHC) to the T-cell receptors (TR) at the surface of T cells. The characterization of the TR/peptide/MHC trimolecular complexes (TR/pMHC) is crucial to the fields of immunology, vaccination, and immunotherapy. In order to facilitate data comparison and cross-referencing between experiments from different laboratories whatever the receptor, the chain type, the domain, or the species, IMGT®, the international ImMunoGeneTics information system® (http://imgt.cines.fr), has developed IMGT-ONTOLOGY, the first ontology in immunogenetics and immunoinformatics. In IMGT/3Dstructure-DB, the IMGT three-dimensional structure database, the molecular characterization of the TR/pMHC is made according to the IMGT Scientific chart rules that are based on the IMGT-ONTOLOGY concepts. IMGT/3Dstructure-DB provides the standardized IMGT gene and allele names (CLASSIFICATION), the standardized IMGT labels (DESCRIPTION), and the IMGT unique numbering (NUMEROTATION). As the IMGT structural unit is the domain, amino acids at conserved positions always have the same number in the IMGT® databases, tools, and Web resources. For the TR α and β chains, the amino acids in contact with the peptide/MHC (pMHC) are defined according to the IMGT unique numbering for V-DOMAIN. The MHC chain cleft that binds the peptide is formed by two groove domains (G-DOMAIN), each one comprising four antiparallel β strands and one α helix. The IMGT unique numbering for G-DOMAIN applies both to the first two domains (G-ALPHA1 and G-ALPHA2) of the MHC class I α chain, and to the first domain (G-ALPHA and G-BETA) of the MHC class II α chain and β chain, respectively. Based on the IMGT unique numbering, we defined 11 contact sites for the analysis of the pMHC contacts. The TR/pMHC contact description, based on the IMGT numbering, can be queried in the IMGT/StucturalQuery tool, at http://imgt.cines.fr. [ABSTRACT FROM AUTHOR]

IMGT®, the international ImMunoGeneTics information system®, was created in 1989 as a high-quality integrated knowledge resource specialized in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complexes (MHC) of human and other vertebrates, and related proteins of the immune system (RPI) which belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF). IMGT® is the international reference in immunogenetics and immunoinformatics. IMGT® combines sequence databases (IMGT/LIGM-DB, IMGT/PRIMER-DB, IMGT/PROTEIN-DB, IMGT/MHC-DB), a genome database (IMGT/E-DB), and a three-dimensional (3D) structure database (IMGT/3Dstructure-DB) with interactive analysis tools (IMGT/V-QUEST, IMGT/JunctionAnalysis) and Web resources comprising 8000 HTML pages (IMGT Repertoire). The accuracy and consistency of IMGT data are based on IMGT-ONTOLOGY, available for biologists and IMGT users in the IMGT Scientific chart and for computer scientists in IMGT-ML, in XML format. IMGT® components (databases, tools, and Web resources) have been developed according to three main biological approaches: the genomic approach that is gene centered, the genetic approach that refers to genes in relation to their polymorphisms, expression, specificity, and evolution, and the structural approach that analyses 3D structures in relation to protein function and recognition sites. We are implementing Web services for the IMGT databases and tools. This is the first step toward IMGTChoreography that will trigger and coordinate dynamic interactions between IMGT Web services in order to process complex significant biological and clinical requests. IMGT® is widely used in fundamental and medical research (repertoire analysis of the IG antibody sites and of the TR recognition sites in autoimmune diseases, infectious diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research situations (IG and TR repertoires in farm and wildlife species), genome diversity and genome evolution studies of the adaptive immune responses, biotechnology related to antibody engineering (single chain Fragment variable (scFv), phage displays, combinatorial libraries, chimeric, humanized, and human antibodies), diagnostics (clonalities, detection and follow-up of residual diseases), and therapeutical approaches (graft, immunotherapy, vaccinology). IMGT® is freely available at http://imgt.cines.fr. [ABSTRACT FROM AUTHOR]

The binding of bound peptide ligands to major histocompatibility complex (MHC) molecules plays a key role in the activation of normal immune responses and is an intricate theoretical problem that remains unsolved. Geometric and energetic complementarities between an MHC molecule and its corresponding bound peptide ligand are critical in determining the stability of the complex. In this context, the introduction of structural information can greatly facilitate our understanding of how well a peptide ligand can associate with a particular MHC molecule. This chapter introduces the use of structural models as a predictive method to determine whether a peptide sequence can bind to a specific MHC allele [ABSTRACT FROM AUTHOR]